This report performs an exploratory data analysis (EDA) to investigate the relationships between multiple numerical and categorical variables within the dataset. The analysis employs several visualization techniques, including grouped boxplots, category-based histograms, and pairplot visualizations to examine pairwise relationships among variables. Through these graphical approaches, the analysis aims to reveal distribution patterns, variability across groups, and potential correlations that may inform further statistical modeling.

Implementasi Principal Component Analysis (PCA) dan Factor Analysis (FA) pada dataset Student Performance (Mathematics) untuk mereduksi dimensi data dan mengidentifikasi faktor yang memengaruhi prestasi matematika siswa. Hasil analisis menunjukkan terbentuk tiga faktor utama yaitu faktor akademik, aktivitas sosial, dan waktu luang.

his page presents an exploratory multivariate analysis of the SIRTUIN6 dataset using R. The analysis includes correlation matrix, variance–covariance matrix, and eigen decomposition to examine the structure of relationships and variance among numerical variables. Prepared as a student worksheet for Multivariate Analysis course.